BACKGROUND: The study aimed to determine the most crucial parameters associated with CVD and employ a novel data ensemble refinement procedure to uncover the optimal pattern of these parameters that can result in a high prediction accuracy.
RESULTS: Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis). The data were analyzed using an artificial intelligence-based algorithm with the primary objective of identifying the optimal pattern of parameters that define CVD. The study highlights the effectiveness of a six-parameter combination in discerning the likelihood of cardiovascular disease using DERGA and Extra Trees algorithms. These parameters, ranked in order of importance, include Platelet-derived Microvesicles (PMV), hypertension, age, smoking, dyslipidemia, and Body Mass Index (BMI). Endothelial and erythrocyte MVs, along with diabetes were the least important predictors. In addition, the highest prediction accuracy achieved is 98.64%. Notably, using PMVs alone yields a 91.32% accuracy, while the optimal model employing all ten parameters, yields a prediction accuracy of 0.9783 (97.83%).
CONCLUSIONS: Our research showcases the efficacy of DERGA, an innovative data ensemble refinement greedy algorithm. DERGA accelerates the assessment of an individual\'s risk of developing CVD, allowing for early diagnosis, significantly reduces the number of required lab tests and optimizes resource utilization. Additionally, it assists in identifying the optimal parameters critical for assessing CVD susceptibility, thereby enhancing our understanding of the underlying mechanisms.

UNASSIGNED: The emergence and rapid spread of multidrug-resistant bacteria (MRB) caused by the excessive use of antibiotics and the development of biofilms have been a growing threat to global public health. Nanoparticles as substitutes for antibiotics were proven to possess substantial abilities for tackling MRB infections via new antimicrobial mechanisms. Particularly, carbon dots (CDs) with unique (bio)physicochemical characteristics have been receiving considerable attention in combating MRB by damaging the bacterial wall, binding to DNA or enzymes, inducing hyperthermia locally, or forming reactive oxygen species.
UNASSIGNED: Herein, how the physicochemical features of various CDs affect their antimicrobial capacity is investigated with the assistance of machine learning (ML) tools.
UNASSIGNED: The synthetic conditions and intrinsic properties of CDs from 121 samples are initially gathered to form the raw dataset, with Minimum inhibitory concentration (MIC) being the output. Four classification algorithms (KNN, SVM, RF, and XGBoost) are trained and validated with the input data. It is found that the ensemble learning methods turn out to be the best on our data. Also, ε-poly(L-lysine) CDs (PL-CDs) were developed to validate the practical application ability of the well-trained ML models in a laboratory with two ensemble models managing the prediction.
UNASSIGNED: Thus, our results demonstrate that ML-based high-throughput theoretical calculation could be used to predict and decode the relationship between CD properties and the anti-bacterial effect, accelerating the development of high-performance nanoparticles and potential clinical translation.

In recent times, various machine learning approaches have been widely employed for effective diagnosis and prediction of diseases like cancer, thyroid, Covid-19, etc. Likewise, Alzheimer\'s (AD) is also one progressive malady that destroys memory and cognitive function over time. Unfortunately, there are no dedicated AI-based solutions for diagnoses of AD to go hand in hand with medical diagnosis, even though multiple factors contribute to the diagnosis, making AI a very viable supplementary diagnostic solution. This paper reports an endeavor to apply various machine learning algorithms like SGD, k-Nearest Neighbors, Logistic Regression, Decision tree, Random Forest, AdaBoost, Neural Network, SVM, and Naïve Bayes on the dataset of affected victims to diagnose Alzheimer\'s disease. Longitudinal collections of subjects from OASIS dataset have been used for prediction. Moreover, some feature selection and dimension reduction methods like Information Gain, Information Gain Ratio, Gini index, Chi-Squared, and PCA are applied to rank different factors and identify the optimum number of factors from the dataset for disease diagnosis. Furthermore, performance is evaluated of each classifier in terms of ROC-AUC, accuracy, F1 score, recall, and precision as well as included comparative analysis between algorithms. Our study suggests that approximately 90% classification accuracy is observed under top-rated four features CDR, SES, nWBV, and EDUC.

Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.

We discuss the implementation challenges of gas sensing systems based on low-frequency noise measurements on chemoresistive sensors. Resistance fluctuations in various gas sensing materials, in a frequency range typically up to a few kHz, can enhance gas sensing by considering its intensity and the slope of power spectral density. The issues of low-frequency noise measurements in resistive gas sensors, specifically in two-dimensional materials exhibiting gas-sensing properties, are considered. We present measurement setups and noise-processing methods for gas detection. The chemoresistive sensors show various DC resistances requiring different flicker noise measurement approaches. Separate noise measurement setups are used for resistances up to a few hundred kΩ and for resistances with much higher values. Noise measurements in highly resistive materials (e.g., MoS2, WS2, and ZrS3) are prone to external interferences but can be modulated using temperature or light irradiation for enhanced sensing. Therefore, such materials are of considerable interest for gas sensing.

Necrotizing enterocolitis (NEC), with the main manifestations of bloody stool, abdominal distension, and vomiting, is one of the leading causes of death in neonates, and early identification and diagnosis are crucial for the prognosis of NEC. The emergence and development of machine learning has provided the potential for early, rapid, and accurate identification of this disease. This article summarizes the algorithms of machine learning recently used in NEC, analyzes the high-risk predictive factors revealed by these algorithms, evaluates the ability and characteristics of machine learning in the etiology, definition, and diagnosis of NEC, and discusses the challenges and prospects for the future application of machine learning in NEC.
新生儿坏死性小肠结肠炎（neonatal necrotizing enterocolitis，NEC）以血便、腹胀、呕吐等为主要表现，是导致新生儿死亡的主要原因之一，早期识别和诊断对该病预后极为重要。机器学习的兴起和发展为早期、快速、准确识别该病提供了可能。该文总结近年来机器学习在NEC应用中的算法，分析算法揭示的高危预测因子，评价机器学习在NEC病因回溯、定义、诊断方面的能力和特点，探讨机器学习在NEC未来应用中的挑战及前景。.

Electromyography (EMG) is a form of biological information, which is used in many fields to help people study human muscle movement, especially in the study of bionic hands. EMG signals can be used to explain the activity at a certain moment through the signal changes of human muscles, and it is a very complex signal, so processing it is very important. The process of EMG signals can be divided into acquisition, pre-processing, feature extraction, and classification. Not all signal channels are useful in EMG acquisition, and it is important to select useful signals among them. Therefore, this study proposes a feature extraction method to extract the most representative two-channel signals from the eight-channel signals. In this paper, the traditional principal component analysis method and support vector machine feature elimination are used to extract signal channels. At the same time, a new method, correlation heat map, is proposed to implement feature extraction method by using three methods, and three classification algorithms of K-nearest neighbor, random forest, and support vector machine are used to verify. The results show that the classification accuracy of the proposed method is better than that of the other two traditional methods.

This paper proposes a new method that can identify and predict financial fraud among listed companies based on machine learning. We collected 18,060 transactions and 363 indicators of finance, including 362 financial variables and a class variable. Then, we eliminated 9 indicators which were not related to financial fraud and processed the missing values. After that, we extracted 13 indicators from 353 indicators which have a big impact on financial fraud based on multiple feature selection models and the frequency of occurrence of features in all algorithms. Then, we established five single classification models and three ensemble models for the prediction of financial fraud records of listed companies, including LR, RF, XGBOOST, SVM, and DT and ensemble models with a voting classifier. Finally, we chose the optimal single model from five machine learning algorithms and the best ensemble model among all hybrid models. In choosing the model parameter, optimal parameters were selected by using the grid search method and comparing several evaluation metrics of models. The results determined the accuracy of the optimal single model to be in a range from 97% to 99%, and that of the ensemble models as higher than 99%. This shows that the optimal ensemble model performs well and can efficiently predict and detect fraudulent activity of companies. Thus, a hybrid model which combines a logistic regression model with an XGBOOST model is the best among all models. In the future, it will not only be able to predict fraudulent behavior in company management but also reduce the burden of doing so.

The performance of classical security authentication models can be severely affected by imperfect channel estimation as well as time-varying communication links. The commonly used approach of statistical decisions for the physical layer authenticator faces significant challenges in a dynamically changing, non-stationary environment. To address this problem, this paper introduces a deep learning-based authentication approach to learn and track the variations of channel characteristics, and thus improving the adaptability and convergence of the physical layer authentication. Specifically, an intelligent detection framework based on a Convolutional-Long Short-Term Memory (Convolutional-LSTM) network is designed to deal with channel differences without knowing the statistical properties of the channel. Both the robustness and the detection performance of the learning authentication scheme are analyzed, and extensive simulations and experiments show that the detection accuracy in time-varying environments is significantly improved.

While the RT-PCR is the silver bullet test for confirming the COVID-19 infection, it is limited by the lack of reagents, time-consuming, and the need for specialized labs. As an alternative, most of the prior studies have focused on Chest CT images and Chest X-Ray images using deep learning algorithms. However, these two approaches cannot always be used for patients\' screening due to the radiation doses, high costs, and the low number of available devices. Hence, there is a need for a less expensive and faster diagnostic model to identify the positive and negative cases of COVID-19. Therefore, this study develops six predictive models for COVID-19 diagnosis using six different classifiers (i.e., BayesNet, Logistic, IBk, CR, PART, and J48) based on 14 clinical features. This study retrospected 114 cases from the Taizhou hospital of Zhejiang Province in China. The results showed that the CR meta-classifier is the most accurate classifier for predicting the positive and negative COVID-19 cases with an accuracy of 84.21%. The results could help in the early diagnosis of COVID-19, specifically when the RT-PCR kits are not sufficient for testing the infection and assist countries, specifically the developing ones that suffer from the shortage of RT-PCR tests and specialized laboratories.